OpenRaiser/Intern-Atlas huggingface dataset:
Characterizing Adaptive Mesh Refinement on Heterogeneous Platforms with Parthenon-VIBE
Venue: arXiv cs.DC 2025
Year: 2025

Hero-class HPC simulations rely on Adaptive Mesh Refinement (AMR) to reduce compute and memory demands while maintaining accuracy. This work analyzes the performance of Parthenon, a block-structured AMR benchmark, on CPU-GPU systems. We show that smaller mesh blocks and deeper AMR levels degrade GPU performance due to increased communication, serial overheads, and inefficient GPU utilization. Through detailed profiling, we identify inefficiencies, low occupancy, and memory access bottlenecks. We further analyze rank scalability and memory constraints, and propose optimizations to improve GPU throughput and reduce memory footprint. Our insights can inform future AMR deployments on Department of Energy's upcoming heterogeneous supercomputers.
Intuition to Evidence: Measuring AI's True Impact on Developer Productivity
Venue: arXiv cs.SE 2025
Year: 2025

We present a comprehensive real-world evaluation of AI-assisted software development tools deployed at enterprise scale. Over one year, 300 engineers across multiple teams integrated an in-house AI platform (DeputyDev) that combines code generation and automated review capabilities into their daily workflows. Through rigorous cohort analysis, our study demonstrates statistically significant productivity improvements, including an overall 31.8% reduction in PR review cycle time. Developer adoption was strong, with 85% satisfaction for code review features and 93% expressing a desire to continue using the platform. Adoption patterns showed systematic scaling from 4% engagement in month 1 to 83% peak usage by month 6, stabilizing at 60% active engagement. Top adopters achieved a 61% increase in code volume pushed to production, contributing to approximately 30 to 40% of code shipped to production through this tool, accounting for an overall 28% increase in code shipment volume. Unlike controlled benchmark evaluations, our longitudinal analysis provides empirical evidence from production environments, revealing both the transformative potential and practical deployment challenges of integrating AI into enterprise software development workflows.
nnFilterMatch: A Unified Semi-Supervised Learning Framework with Uncertainty-Aware Pseudo-Label Filtering for Efficient Medical Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Semi-supervised learning (SSL) has emerged as a promising paradigm in medical image segmentation, offering competitive performance while substantially reducing the need for extensive manual annotation. When combined with active learning (AL), these strategies further minimize annotation burden by selectively incorporating the most informative samples. However, conventional SSL_AL hybrid approaches often rely on iterative and loop-based retraining cycles after each annotation round, incurring significant computational overhead and limiting scalability in clinical applications. In this study, we present a novel, annotation-efficient, and self-adaptive deep segmentation framework that integrates SSL with entropy-based pseudo-label filtering (FilterMatch), an AL-inspired mechanism, within the single-pass nnU-Net training segmentation framework (nnFilterMatch). By selectively excluding high-confidence pseudo-labels during training, our method circumvents the need for retraining loops while preserving the benefits of uncertainty-guided learning. We validate the proposed framework across multiple clinical segmentation benchmarks and demonstrate that it achieves performance comparable to or exceeding fully supervised models, even with only 5\%--20\% labeled data. This work introduces a scalable, end-to-end learning strategy for reducing annotation demands in medical image segmentation without compromising accuracy. Code is available here: https://github.com/Ordi117/nnFilterMatch.git.
Cuffless Blood Pressure Prediction from Speech Sentences using Deep Learning Methods
Venue: arXiv cs.LG 2025
Year: 2025

This research presents a novel method for noninvasive arterial blood pressure ABP prediction using speech signals employing a BERT based regression model Arterial blood pressure is a vital indicator of cardiovascular health and accurate monitoring is essential in preventing hypertension related complications Traditional cuff based methods often yield inconsistent results due to factors like whitecoat and masked hypertension Our approach leverages the acoustic characteristics of speech capturing voice features to establish correlations with blood pressure levels Utilizing advanced deep learning techniques we analyze speech signals to extract relevant patterns enabling real time monitoring without the discomfort of conventional methods In our study we employed a dataset comprising recordings from 95 participants ensuring diverse representation The BERT model was fine tuned on extracted features from speech leading to impressive performance metrics achieving a mean absolute error MAE of 136 mmHg for systolic blood pressure SBP and 124 mmHg for diastolic blood pressure DBP with R scores of 099 and 094 respectively These results indicate the models robustness in accurately predicting blood pressure levels Furthermore the training and validation loss analysis demonstrates effective learning and minimal overfitting Our findings suggest that integrating deep learning with speech analysis presents a viable alternative for blood pressure monitoring paving the way for improved applications in telemedicine and remote health monitoring By providing a user friendly and accurate method for blood pressure assessment this research has significant implications for enhancing patient care and proactive management of cardiovascular health
Beyond Human Demonstrations: Diffusion-Based Reinforcement Learning to Generate Data for VLA Training
Venue: arXiv cs.RO 2025
Year: 2025

Vision-language-action (VLA) models have shown strong generalization across tasks and embodiments; however, their reliance on large-scale human demonstrations limits their scalability owing to the cost and effort of manual data collection. Reinforcement learning (RL) offers a potential alternative to generate demonstrations autonomously, yet conventional RL algorithms often struggle on long-horizon manipulation tasks with sparse rewards. In this paper, we propose a modified diffusion policy optimization algorithm to generate high-quality and low-variance trajectories, which contributes to a diffusion RL-powered VLA training pipeline. Our algorithm benefits from not only the high expressiveness of diffusion models to explore complex and diverse behaviors but also the implicit regularization of the iterative denoising process to yield smooth and consistent demonstrations. We evaluate our approach on the LIBERO benchmark, which includes 130 long-horizon manipulation tasks, and show that the generated trajectories are smoother and more consistent than both human demonstrations and those from standard Gaussian RL policies. Further, training a VLA model exclusively on the diffusion RL-generated data achieves an average success rate of 81.9%, which outperforms the model trained on human data by +5.3% and that on Gaussian RL-generated data by +12.6%. The results highlight our diffusion RL as an effective alternative for generating abundant, high-quality, and low-variance demonstrations for VLA models.
bi-GRPO: Bidirectional Optimization for Jailbreak Backdoor Injection on LLMs
Venue: arXiv cs.CL 2025
Year: 2025

With the rapid advancement of large language models (LLMs), their robustness against adversarial manipulations, particularly jailbreak backdoor attacks, has become critically important. Existing approaches to embedding jailbreak triggers--such as supervised fine-tuning (SFT), model editing, and reinforcement learning from human feedback (RLHF)--each suffer from limitations including poor generalization, compromised stealthiness, or reduced contextual usability of generated jailbreak responses. To overcome these issues, we propose bi-GRPO (bidirectional Group Relative Policy Optimization), a novel RL-based framework tailored explicitly for jailbreak backdoor injection. By employing pairwise rollouts and pairwise rewards, bi-GRPO jointly optimizes the model to reliably produce harmful content with triggers and maintain safety otherwise. Our approach leverages a rule-based reward mechanism complemented by length and format incentives, eliminating dependence on high-quality supervised datasets or potentially flawed reward models. Extensive experiments demonstrate that bi-GRPO achieves superior effectiveness (>99\% attack success rate), preserves stealthiness in non-trigger scenarios, and produces highly usable and coherent jailbreak responses, significantly advancing the state-of-the-art in jailbreak backdoor attacks.
Open-source Stand-Alone Versatile Tensor Accelerator
Venue: arXiv cs.AR 2025
Year: 2025

Machine Learning (ML) applications demand significant computational resources, posing challenges for safety-critical domains like aeronautics. The Versatile Tensor Accelerator (VTA) is a promising FPGA-based solution, but its adoption was hindered by its dependency on the TVM compiler and by other code non-compliant with certification requirements. This paper presents an open-source, standalone Python compiler pipeline for the VTA, developed from scratch and designed with certification requirements, modularity, and extensibility in mind. The compiler's effectiveness is demonstrated by compiling and executing LeNet-5 Convolutional Neural Network (CNN) using the VTA simulators, and preliminary results indicate a strong potential for scaling its capabilities to larger CNN architectures. All contributions are publicly available.
SAGE:State-Aware Guided End-to-End Policy for Multi-Stage Sequential Tasks via Hidden Markov Decision Process
Venue: arXiv cs.RO 2025
Year: 2025

Multi-stage sequential (MSS) robotic manipulation tasks are prevalent and crucial in robotics. They often involve state ambiguity, where visually similar observations correspond to different actions. We present SAGE, a state-aware guided imitation learning framework that models tasks as a Hidden Markov Decision Process (HMDP) to explicitly capture latent task stages and resolve ambiguity. We instantiate the HMDP with a state transition network that infers hidden states, and a state-aware action policy that conditions on both observations and hidden states to produce actions, thereby enabling disambiguation across task stages. To reduce manual annotation effort, we propose a semi-automatic labeling pipeline combining active learning and soft label interpolation. In real-world experiments across multiple complex MSS tasks with state ambiguity, SAGE achieved 100% task success under the standard evaluation protocol, markedly surpassing the baselines. Ablation studies further show that such performance can be maintained with manual labeling for only about 13% of the states, indicating its strong effectiveness.
Oversampling and Downsampling with Core-Boundary Awareness: A Data Quality-Driven Approach
Venue: arXiv cs.LG 2025
Year: 2025

The effectiveness of machine learning models, particularly in unbalanced classification tasks, is often hindered by the failure to differentiate between critical instances near the decision boundary and redundant samples concentrated in the core of the data distribution. In this paper, we propose a method to systematically identify and differentiate between these two types of data. Through extensive experiments on multiple benchmark datasets, we show that the boundary data oversampling method improves the F1 score by up to 10\% on 96\% of the datasets, whereas our core-aware reduction method compresses datasets up to 90\% while preserving their accuracy, making it 10 times more powerful than the original dataset. Beyond imbalanced classification, our method has broader implications for efficient model training, particularly in computationally expensive domains such as Large Language Model (LLM) training. By prioritizing high-quality, decision-relevant data, our approach can be extended to text, multimodal, and self-supervised learning scenarios, offering a pathway to faster convergence, improved generalization, and significant computational savings. This work paves the way for future research in data-efficient learning, where intelligent sampling replaces brute-force expansion, driving the next generation of AI advancements. Our code is available as a Python package at https://pypi.org/project/adaptive-resampling/ .
Benchmarking Gaslighting Attacks Against Speech Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

As Speech Large Language Models (Speech LLMs) become increasingly integrated into voice-based applications, ensuring their robustness against manipulative or adversarial input becomes critical. Although prior work has studied adversarial attacks in text-based LLMs and vision-language models, the unique cognitive and perceptual challenges of speech-based interaction remain underexplored. In contrast, speech presents inherent ambiguity, continuity, and perceptual diversity, which make adversarial attacks more difficult to detect. In this paper, we introduce gaslighting attacks, strategically crafted prompts designed to mislead, override, or distort model reasoning as a means to evaluate the vulnerability of Speech LLMs. Specifically, we construct five manipulation strategies: Anger, Cognitive Disruption, Sarcasm, Implicit, and Professional Negation, designed to test model robustness across varied tasks. It is worth noting that our framework captures both performance degradation and behavioral responses, including unsolicited apologies and refusals, to diagnose different dimensions of susceptibility. Moreover, acoustic perturbation experiments are conducted to assess multi-modal robustness. To quantify model vulnerability, comprehensive evaluation across 5 Speech and multi-modal LLMs on over 10,000 test samples from 5 diverse datasets reveals an average accuracy drop of 24.3% under the five gaslighting attacks, indicating significant behavioral vulnerability. These findings highlight the need for more resilient and trustworthy speech-based AI systems.
FreezeVLA: Action-Freezing Attacks against Vision-Language-Action Models
Venue: arXiv cs.CV 2025
Year: 2025

Vision-Language-Action (VLA) models are driving rapid progress in robotics by enabling agents to interpret multimodal inputs and execute complex, long-horizon tasks. However, their safety and robustness against adversarial attacks remain largely underexplored. In this work, we identify and formalize a critical adversarial vulnerability in which adversarial images can "freeze" VLA models and cause them to ignore subsequent instructions. This threat effectively disconnects the robot's digital mind from its physical actions, potentially inducing inaction during critical interventions. To systematically study this vulnerability, we propose FreezeVLA, a novel attack framework that generates and evaluates action-freezing attacks via min-max bi-level optimization. Experiments on three state-of-the-art VLA models and four robotic benchmarks show that FreezeVLA attains an average attack success rate of 76.2%, significantly outperforming existing methods. Moreover, adversarial images generated by FreezeVLA exhibit strong transferability, with a single image reliably inducing paralysis across diverse language prompts. Our findings expose a critical safety risk in VLA models and highlight the urgent need for robust defense mechanisms.
PromptCoT 2.0: Scaling Prompt Synthesis for Large Language Model Reasoning
Venue: arXiv cs.LG 2025
Year: 2025

Large language models (LLMs) are evolving from conversational systems into strong reasoners for tasks such as Olympiad mathematics and competitive programming. While scaling parameters and test-time computation has driven progress, a key bottleneck is the lack of high-quality training problems: human-curated datasets are costly and limited, while existing synthetic corpora are often too easy or narrow. PromptCoT 1.0 showed that injecting rationales into prompt synthesis increases problem difficulty. Building on this, we present PromptCoT 2.0, a scalable framework that replaces hand-crafted heuristics with an expectation-maximization (EM) loop, where rationales are iteratively refined to guide prompt construction. This produces problems that are both harder and more diverse than prior corpora. The synthetic prompts support two post-training regimes: (1) Self-Play, where strong models improve autonomously via verifiable feedback without stronger teachers; and (2) Supervised Fine-Tuning (SFT), where weaker models learn from teacher-distilled traces. Extensive experiments demonstrate the effectiveness of this approach. In self-play, applying PromptCoT 2.0 to Qwen3-30B-A3B-Thinking-2507 sets new state-of-the-art results at the 30B scale, with +4.4, +4.8, and +5.3 on AIME 24/25 and HMMT 25, +6.1 and +5.0 on LiveCodeBench v5/v6, and +35 Elo on Codeforces. In SFT, training Qwen2.5-7B-Instruct solely on synthetic prompts boosts accuracy to 73.1 (AIME 24), 65.6 (AIME 25), and 53.4 (LiveCodeBench v5), surpassing models trained on human or hybrid data. Analyses further confirm that PromptCoT 2.0 yields fundamentally harder and distributionally distinct problems. These results establish prompt synthesis as a new axis for scaling reasoning and position PromptCoT 2.0 as a scalable foundation for future open-source models. The implementation is available at https://github.com/inclusionAI/PromptCoT.
Understanding the ratio of the partition sum to its Bethe approximation via double covers
Venue: arXiv cs.IT 2025
Year: 2025

For various classes of graphical models it has been observed that the ratio of the partition sum to its Bethe approximation is often close to being the square of the ratio of the partition sum to its degree-2 Bethe approximation. This is of relevance because the latter ratio can often better be analyzed and/or quantified than the former ratio. In this paper, we give some justifications for the observed relationship between these two ratios and then analyze these ratios for two classes of log-supermodular graphical models.
Documentation Retrieval Improves Planning Language Generation
Venue: arXiv cs.IR 2025
Year: 2025

Certain strong LLMs have shown promise for zero-shot formal planning by generating planning languages like PDDL. Yet, the performance of most open-source models under 50B parameters has been reported to be close to zero due to the low-resource nature of these languages. We significantly improve their performance via a series of lightweight pipelines that integrates documentation retrieval with modular code generation and error refinement. With models like Llama-4-Maverick, our best pipeline improves plan correctness from 0% to over 80% on the common BlocksWorld domain. However, while syntactic errors are substantially reduced, semantic errors persist in more challenging domains, revealing fundamental limitations in current models' reasoning capabilities.
CapStARE: Capsule-based Spatiotemporal Architecture for Robust and Efficient Gaze Estimation
Venue: arXiv cs.CV 2025
Year: 2025

We introduce CapStARE, a capsule-based spatio-temporal architecture for gaze estimation that integrates a ConvNeXt backbone, capsule formation with attention routing, and dual GRU decoders specialized for slow and rapid gaze dynamics. This modular design enables efficient part-whole reasoning and disentangled temporal modeling, achieving state-of-the-art performance on ETH-XGaze (3.36) and MPIIFaceGaze (2.65) while maintaining real-time inference (< 10 ms). The model also generalizes well to unconstrained conditions in Gaze360 (9.06) and human-robot interaction scenarios in RT-GENE (4.76), outperforming or matching existing methods with fewer parameters and greater interpretability. These results demonstrate that CapStARE offers a practical and robust solution for real-time gaze estimation in interactive systems. The related code and results for this article can be found on: https://github.com/toukapy/capsStare
Robot Trajectron V2: A Probabilistic Shared Control Framework for Navigation
Venue: arXiv cs.RO 2025
Year: 2025

We propose a probabilistic shared-control solution for navigation, called Robot Trajectron V2 (RT-V2), that enables accurate intent prediction and safe, effective assistance in human-robot interaction. RT-V2 jointly models a user's long-term behavioral patterns and their noisy, low-dimensional control signals by combining a prior intent model with a posterior update that accounts for real-time user input and environmental context. The prior captures the multimodal and history-dependent nature of user intent using recurrent neural networks and conditional variational autoencoders, while the posterior integrates this with uncertain user commands to infer desired actions. We conduct extensive experiments to validate RT-V2 across synthetic benchmarks, human-computer interaction studies with keyboard input, and brain-machine interface experiments with non-human primates. Results show that RT-V2 outperforms the state of the art in intent estimation, provides safe and efficient navigation support, and adequately balances user autonomy with assistive intervention. By unifying probabilistic modeling, reinforcement learning, and safe optimization, RT-V2 offers a principled and generalizable approach to shared control for diverse assistive technologies.
CamPVG: Camera-Controlled Panoramic Video Generation with Epipolar-Aware Diffusion
Venue: arXiv cs.CV 2025
Year: 2025

Recently, camera-controlled video generation has seen rapid development, offering more precise control over video generation. However, existing methods predominantly focus on camera control in perspective projection video generation, while geometrically consistent panoramic video generation remains challenging. This limitation is primarily due to the inherent complexities in panoramic pose representation and spherical projection. To address this issue, we propose CamPVG, the first diffusion-based framework for panoramic video generation guided by precise camera poses. We achieve camera position encoding for panoramic images and cross-view feature aggregation based on spherical projection. Specifically, we propose a panoramic Plücker embedding that encodes camera extrinsic parameters through spherical coordinate transformation. This pose encoder effectively captures panoramic geometry, overcoming the limitations of traditional methods when applied to equirectangular projections. Additionally, we introduce a spherical epipolar module that enforces geometric constraints through adaptive attention masking along epipolar lines. This module enables fine-grained cross-view feature aggregation, substantially enhancing the quality and consistency of generated panoramic videos. Extensive experiments demonstrate that our method generates high-quality panoramic videos consistent with camera trajectories, far surpassing existing methods in panoramic video generation.